Overview

Dataset statistics

Number of variables37
Number of observations806415
Missing cells18027
Missing cells (%)0.1%
Duplicate rows39946
Duplicate rows (%)5.0%
Total size in memory212.3 MiB
Average record size in memory276.0 B

Variable types

Categorical20
Numeric13
Boolean4

Alerts

cancelled has constant value "False" Constant
bus.wifi has constant value "False" Constant
Dataset has 39946 (5.0%) duplicate rowsDuplicates
Stop_Name has a high cardinality: 1487 distinct values High cardinality
street.name has a high cardinality: 290 distinct values High cardinality
cross-street.name has a high cardinality: 724 distinct values High cardinality
Bus_Variant has a high cardinality: 242 distinct values High cardinality
Destination_Name has a high cardinality: 150 distinct values High cardinality
Route_Name has a high cardinality: 225 distinct values High cardinality
Route_Description has a high cardinality: 81 distinct values High cardinality
key has a high cardinality: 16008 distinct values High cardinality
times.departure.scheduled has a high cardinality: 7389 distinct values High cardinality
times.departure.estimated has a high cardinality: 7391 distinct values High cardinality
variant.key has a high cardinality: 108 distinct values High cardinality
variant.name has a high cardinality: 107 distinct values High cardinality
times.arrival.scheduled has a high cardinality: 7389 distinct values High cardinality
times.arrival.estimated has a high cardinality: 7391 distinct values High cardinality
Stop_Number is highly correlated with centre.utm.y and 1 other fieldsHigh correlation
centre.utm.x is highly correlated with centre.geographic.longitudeHigh correlation
centre.utm.y is highly correlated with Stop_Number and 1 other fieldsHigh correlation
centre.geographic.latitude is highly correlated with Stop_Number and 1 other fieldsHigh correlation
centre.geographic.longitude is highly correlated with centre.utm.xHigh correlation
Delay_Time is highly correlated with IsDelayedHigh correlation
IsDelayed is highly correlated with Delay_TimeHigh correlation
Stop_Number is highly correlated with centre.utm.y and 1 other fieldsHigh correlation
centre.utm.x is highly correlated with centre.geographic.longitudeHigh correlation
centre.utm.y is highly correlated with Stop_Number and 1 other fieldsHigh correlation
centre.geographic.latitude is highly correlated with Stop_Number and 1 other fieldsHigh correlation
centre.geographic.longitude is highly correlated with centre.utm.xHigh correlation
Number_of_Stoppages is highly correlated with Variants_CountHigh correlation
Variants_Count is highly correlated with Number_of_StoppagesHigh correlation
centre.utm.x is highly correlated with centre.geographic.longitudeHigh correlation
centre.utm.y is highly correlated with centre.geographic.latitudeHigh correlation
centre.geographic.latitude is highly correlated with centre.utm.yHigh correlation
centre.geographic.longitude is highly correlated with centre.utm.xHigh correlation
Delay_Time is highly correlated with IsDelayedHigh correlation
IsDelayed is highly correlated with Delay_TimeHigh correlation
bus.wifi is highly correlated with bus.bike-rack and 9 other fieldsHigh correlation
bus.bike-rack is highly correlated with bus.wifi and 1 other fieldsHigh correlation
cross-street.type is highly correlated with bus.wifi and 1 other fieldsHigh correlation
Route_Description is highly correlated with bus.wifi and 2 other fieldsHigh correlation
side is highly correlated with bus.wifi and 1 other fieldsHigh correlation
direction is highly correlated with bus.wifi and 1 other fieldsHigh correlation
cancelled is highly correlated with bus.wifi and 9 other fieldsHigh correlation
coverage is highly correlated with bus.wifi and 2 other fieldsHigh correlation
Types_of_Features_Count is highly correlated with bus.wifi and 1 other fieldsHigh correlation
IsDelayed is highly correlated with bus.wifi and 1 other fieldsHigh correlation
street.type is highly correlated with bus.wifi and 1 other fieldsHigh correlation
Stop_Number is highly correlated with street.type and 10 other fieldsHigh correlation
direction is highly correlated with street.type and 2 other fieldsHigh correlation
street.key is highly correlated with street.type and 2 other fieldsHigh correlation
street.type is highly correlated with Stop_Number and 10 other fieldsHigh correlation
cross-street.key is highly correlated with street.type and 1 other fieldsHigh correlation
cross-street.type is highly correlated with Stop_Number and 10 other fieldsHigh correlation
centre.utm.x is highly correlated with Stop_Number and 8 other fieldsHigh correlation
centre.utm.y is highly correlated with Stop_Number and 9 other fieldsHigh correlation
centre.geographic.latitude is highly correlated with Stop_Number and 9 other fieldsHigh correlation
centre.geographic.longitude is highly correlated with Stop_Number and 8 other fieldsHigh correlation
Variants_Serviced_Count is highly correlated with cross-street.type and 3 other fieldsHigh correlation
Number_of_Stoppages is highly correlated with Stop_Number and 8 other fieldsHigh correlation
Types_of_Features_Count is highly correlated with Variants_Serviced_Count and 1 other fieldsHigh correlation
Route_Number is highly correlated with Stop_Number and 5 other fieldsHigh correlation
Variants_Count is highly correlated with Stop_Number and 9 other fieldsHigh correlation
Route_Description is highly correlated with Stop_Number and 14 other fieldsHigh correlation
coverage is highly correlated with Stop_Number and 6 other fieldsHigh correlation
bus.key is highly correlated with bus.bike-rackHigh correlation
bus.bike-rack is highly correlated with bus.keyHigh correlation
side has 10193 (1.3%) missing values Missing
Delay_Time has 754106 (93.5%) zeros Zeros

Reproduction

Analysis started2022-12-27 23:51:31.318470
Analysis finished2022-12-27 23:54:40.730600
Duration3 minutes and 9.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Stop_Name
Categorical

HIGH CARDINALITY

Distinct1487
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Northbound De Baets at Camiel Sys North
 
12635
Eastbound Headmaster at Leatherwood East
 
10208
Eastbound Blue Mountain at Copperstone
 
9225
Westbound Pandora at Hoka
 
6342
Eastbound Camiel Sys at Ray Marius
 
6194
Other values (1482)
761811 

Length

Max length70
Median length31
Mean length32.14702852
Min length22

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowEastbound Graham at Smith
2nd rowEastbound Graham at Smith
3rd rowEastbound Graham at Smith
4th rowEastbound Graham at Smith
5th rowEastbound Graham at Smith

Common Values

ValueCountFrequency (%)
Northbound De Baets at Camiel Sys North12635
 
1.6%
Eastbound Headmaster at Leatherwood East10208
 
1.3%
Eastbound Blue Mountain at Copperstone9225
 
1.1%
Westbound Pandora at Hoka6342
 
0.8%
Eastbound Camiel Sys at Ray Marius6194
 
0.8%
Eastbound Dafoe at U of M Station6020
 
0.7%
Southbound Ashworth at Coombs5880
 
0.7%
Northbound Balmoral at Balmoral Station (42)5688
 
0.7%
Southbound De La Seigneurie at Desjardins5250
 
0.7%
Westbound Morley at Osborne5208
 
0.6%
Other values (1477)733765
91.0%

Length

2022-12-27T17:54:40.953848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
at806415
21.3%
eastbound237464
 
6.3%
westbound218341
 
5.8%
southbound187499
 
4.9%
northbound163111
 
4.3%
de50999
 
1.3%
east41984
 
1.1%
west33171
 
0.9%
st30985
 
0.8%
la28296
 
0.7%
Other values (891)1990329
52.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Stop_Number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1491
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41447.24655
Minimum10010
Maximum62017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:41.075858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10010
5-th percentile10117
Q130807
median50316
Q350873
95-th percentile60845
Maximum62017
Range52007
Interquartile range (IQR)20066

Descriptive statistics

Standard deviation14901.21393
Coefficient of variation (CV)0.3595224091
Kurtosis-0.4554647988
Mean41447.24655
Median Absolute Deviation (MAD)9690
Skewness-0.8281039003
Sum3.342368132 × 1010
Variance222046176.6
MonotonicityNot monotonic
2022-12-27T17:54:41.230844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5087412635
 
1.6%
4062610208
 
1.3%
501409225
 
1.1%
400996342
 
0.8%
509946194
 
0.8%
612546020
 
0.7%
509445880
 
0.7%
108465688
 
0.7%
507835250
 
0.7%
100335208
 
0.6%
Other values (1481)733765
91.0%
ValueCountFrequency (%)
100101800
0.2%
100112304
0.3%
10012702
 
0.1%
10013366
 
< 0.1%
10014492
 
0.1%
100171044
0.1%
10018408
 
0.1%
10021240
 
< 0.1%
10022654
 
0.1%
1002396
 
< 0.1%
ValueCountFrequency (%)
620172296
 
0.3%
62013238
 
< 0.1%
6201256
 
< 0.1%
62011112
 
< 0.1%
620107
 
< 0.1%
6200312
 
< 0.1%
6128210
 
< 0.1%
6128044
 
< 0.1%
61279110
 
< 0.1%
612546020
0.7%

direction
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Eastbound
237464 
Westbound
218341 
Southbound
187499 
Northbound
163111 

Length

Max length10
Median length9
Mean length9.434776139
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastbound
2nd rowEastbound
3rd rowEastbound
4th rowEastbound
5th rowEastbound

Common Values

ValueCountFrequency (%)
Eastbound237464
29.4%
Westbound218341
27.1%
Southbound187499
23.3%
Northbound163111
20.2%

Length

2022-12-27T17:54:41.342848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-27T17:54:41.524424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
eastbound237464
29.4%
westbound218341
27.1%
southbound187499
23.3%
northbound163111
20.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

side
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing10193
Missing (%)1.3%
Memory size12.3 MiB
Nearside
361958 
Farside
296410 
Nearside Opposite
71144 
Farside Opposite
54084 
Direct Opposite
 
12626

Length

Max length17
Median length8
Mean length9.086305076
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNearside
2nd rowNearside
3rd rowNearside
4th rowNearside
5th rowNearside

Common Values

ValueCountFrequency (%)
Nearside361958
44.9%
Farside296410
36.8%
Nearside Opposite71144
 
8.8%
Farside Opposite54084
 
6.7%
Direct Opposite12626
 
1.6%
(Missing)10193
 
1.3%

Length

2022-12-27T17:54:41.642425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-27T17:54:41.718446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
nearside433102
46.4%
farside350494
37.5%
opposite137854
 
14.8%
direct12626
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

street.key
Real number (ℝ≥0)

HIGH CORRELATION

Distinct294
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1618162.629
Minimum4
Maximum70002223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:41.867422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile171
Q1997
median2063
Q33136
95-th percentile3879
Maximum70002223
Range70002219
Interquartile range (IQR)2139

Descriptive statistics

Standard deviation9132418.636
Coefficient of variation (CV)5.643696421
Kurtosis30.05817891
Mean1618162.629
Median Absolute Deviation (MAD)1066
Skewness5.58858277
Sum1.304910616 × 1012
Variance8.340107014 × 1013
MonotonicityNot monotonic
2022-12-27T17:54:42.010414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99726389
 
3.3%
5226360
 
3.3%
313622655
 
2.8%
305921569
 
2.7%
29621125
 
2.6%
169420800
 
2.6%
274718979
 
2.4%
357818726
 
2.3%
112218073
 
2.2%
255117832
 
2.2%
Other values (284)593907
73.6%
ValueCountFrequency (%)
41217
 
0.2%
36235
 
< 0.1%
5226360
3.3%
562930
 
0.4%
9210
 
< 0.1%
118966
 
0.1%
1287730
 
1.0%
14114
 
< 0.1%
17116901
2.1%
174930
 
0.1%
ValueCountFrequency (%)
70002223594
 
0.1%
700021632406
0.3%
50002021534
 
0.1%
500020061530
0.2%
5000200596
 
< 0.1%
500020042772
0.3%
50001002148
 
< 0.1%
500003021800
0.2%
500002971040
 
0.1%
50000291380
 
< 0.1%

street.name
Categorical

HIGH CARDINALITY

Distinct290
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
De La Seigneurie Boulevard
 
26389
Aldgate Road
 
26360
Rougeau Avenue
 
22655
Pandora Avenue
 
21815
River Road
 
21569
Other values (285)
687627 

Length

Max length26
Median length14
Mean length15.01888978
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowGraham Avenue
2nd rowGraham Avenue
3rd rowGraham Avenue
4th rowGraham Avenue
5th rowGraham Avenue

Common Values

ValueCountFrequency (%)
De La Seigneurie Boulevard26389
 
3.3%
Aldgate Road26360
 
3.3%
Rougeau Avenue22655
 
2.8%
Pandora Avenue21815
 
2.7%
River Road21569
 
2.7%
Beaverhill Boulevard21125
 
2.6%
Headmaster Row20800
 
2.6%
Taylor Avenue18726
 
2.3%
Dugald Road18073
 
2.2%
Morley Avenue17832
 
2.2%
Other values (280)591071
73.3%

Length

2022-12-27T17:54:42.178437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
avenue214118
 
11.8%
road196165
 
10.8%
street130691
 
7.2%
boulevard121365
 
6.7%
drive67847
 
3.7%
de43709
 
2.4%
la26719
 
1.5%
seigneurie26389
 
1.4%
aldgate26360
 
1.4%
taylor23774
 
1.3%
Other values (312)943986
51.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

street.type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing5211
Missing (%)0.6%
Memory size12.3 MiB
Avenue
214118 
Road
203893 
Street
130691 
Boulevard
121365 
Drive
67847 
Other values (12)
63290 

Length

Max length9
Median length6
Mean length5.781548769
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAvenue
2nd rowAvenue
3rd rowAvenue
4th rowAvenue
5th rowAvenue

Common Values

ValueCountFrequency (%)
Avenue214118
26.6%
Road203893
25.3%
Street130691
16.2%
Boulevard121365
15.0%
Drive67847
 
8.4%
Row20800
 
2.6%
Crescent10659
 
1.3%
Place8910
 
1.1%
Loop8212
 
1.0%
Terminal6997
 
0.9%
Other values (7)7712
 
1.0%
(Missing)5211
 
0.6%

Length

2022-12-27T17:54:42.321436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
avenue214118
26.7%
road203893
25.4%
street130691
16.3%
boulevard121365
15.1%
drive67847
 
8.5%
row20800
 
2.6%
crescent10659
 
1.3%
place8910
 
1.1%
loop8212
 
1.0%
terminal6997
 
0.9%
Other values (7)7712
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cross-street.key
Real number (ℝ≥0)

HIGH CORRELATION

Distinct727
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347556.112
Minimum1
Maximum50002001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:42.429426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile218
Q1976
median2063
Q33062
95-th percentile4173
Maximum50002001
Range50002000
Interquartile range (IQR)2086

Descriptive statistics

Standard deviation4141724.846
Coefficient of variation (CV)11.91670842
Kurtosis139.7341164
Mean347556.112
Median Absolute Deviation (MAD)1052
Skewness11.90519873
Sum2.80274462 × 1011
Variance1.71538847 × 1013
MonotonicityNot monotonic
2022-12-27T17:54:42.560430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61612635
 
1.6%
176611893
 
1.5%
211910416
 
1.3%
28849783
 
1.2%
20399589
 
1.2%
37809528
 
1.2%
9549294
 
1.2%
8619225
 
1.1%
19328868
 
1.1%
33998724
 
1.1%
Other values (717)706460
87.6%
ValueCountFrequency (%)
14193
0.5%
86
 
< 0.1%
16436
 
0.1%
21539
 
0.1%
29192
 
< 0.1%
36258
 
< 0.1%
396
 
< 0.1%
401611
 
0.2%
521211
 
0.2%
65115
 
< 0.1%
ValueCountFrequency (%)
500020012017
0.3%
5000100290
 
< 0.1%
50001001492
 
0.1%
500002622948
0.4%
5000002125
 
< 0.1%
49342908
0.4%
4930192
 
< 0.1%
487456
 
< 0.1%
48687
 
< 0.1%
4855112
 
< 0.1%

cross-street.name
Categorical

HIGH CARDINALITY

Distinct724
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Camiel Sys Street
 
12635
Hoka Street
 
11893
Leatherwood Crescent
 
10416
Plessis Road
 
9783
Lagimodiere Boulevard
 
9589
Other values (719)
752099 

Length

Max length26
Median length14
Mean length14.64330153
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSmith Street
2nd rowSmith Street
3rd rowSmith Street
4th rowSmith Street
5th rowSmith Street

Common Values

ValueCountFrequency (%)
Camiel Sys Street12635
 
1.6%
Hoka Street11893
 
1.5%
Leatherwood Crescent10416
 
1.3%
Plessis Road9783
 
1.2%
Lagimodiere Boulevard9589
 
1.2%
Warde Avenue9528
 
1.2%
Dakota Street9294
 
1.2%
Copperstone Crescent9225
 
1.1%
Kenaston Boulevard8868
 
1.1%
Southglen Boulevard8724
 
1.1%
Other values (714)706460
87.6%

Length

2022-12-27T17:54:42.709433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street209219
 
12.0%
avenue136528
 
7.8%
road117132
 
6.7%
drive103416
 
5.9%
crescent80830
 
4.6%
boulevard53041
 
3.0%
bay34451
 
2.0%
place20535
 
1.2%
park18497
 
1.1%
lane13529
 
0.8%
Other values (764)959795
54.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cross-street.type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing823
Missing (%)0.1%
Memory size12.3 MiB
Street
209219 
Avenue
136528 
Road
120572 
Drive
101399 
Crescent
80706 
Other values (16)
157168 

Length

Max length9
Median length6
Mean length5.704213796
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStreet
2nd rowStreet
3rd rowStreet
4th rowStreet
5th rowStreet

Common Values

ValueCountFrequency (%)
Street209219
25.9%
Avenue136528
16.9%
Road120572
15.0%
Drive101399
12.6%
Crescent80706
 
10.0%
Boulevard53041
 
6.6%
Bay33959
 
4.2%
Place20535
 
2.5%
Lane13529
 
1.7%
Way11142
 
1.4%
Other values (11)24962
 
3.1%

Length

2022-12-27T17:54:42.836430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street209219
26.0%
avenue136528
16.9%
road120572
15.0%
drive101399
12.6%
crescent80706
 
10.0%
boulevard53041
 
6.6%
bay33959
 
4.2%
place20535
 
2.5%
lane13529
 
1.7%
way11142
 
1.4%
Other values (11)24962
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

centre.utm.x
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1427
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean635371.5779
Minimum620638
Maximum646295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:42.944351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum620638
5-th percentile624162
Q1631653
median637128
Q3639803
95-th percentile641828
Maximum646295
Range25657
Interquartile range (IQR)8150

Descriptive statistics

Standard deviation5482.328954
Coefficient of variation (CV)0.00862853981
Kurtosis-0.3106436468
Mean635371.5779
Median Absolute Deviation (MAD)3315
Skewness-0.7532584124
Sum5.12373171 × 1011
Variance30055930.76
MonotonicityNot monotonic
2022-12-27T17:54:43.101443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64135612635
 
1.6%
64003210208
 
1.3%
6408379225
 
1.1%
6425206342
 
0.8%
6408336194
 
0.8%
6344936020
 
0.7%
6377365880
 
0.7%
6327185688
 
0.7%
6396715250
 
0.7%
6340925243
 
0.7%
Other values (1417)733730
91.0%
ValueCountFrequency (%)
6206382223
0.3%
6209497
 
< 0.1%
620978168
 
< 0.1%
620985184
 
< 0.1%
62099228
 
< 0.1%
621127154
 
< 0.1%
621132141
 
< 0.1%
621153320
 
< 0.1%
6211662198
0.3%
621168306
 
< 0.1%
ValueCountFrequency (%)
64629515
 
< 0.1%
646274455
 
0.1%
646258125
 
< 0.1%
646235410
 
0.1%
646209310
 
< 0.1%
64620020
 
< 0.1%
64616895
 
< 0.1%
6459701400
0.2%
645939100
 
< 0.1%
64553741
 
< 0.1%

centre.utm.y
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1408
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5525844.66
Minimum5514217
Maximum5536457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:43.265426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5514217
5-th percentile5519264
Q15521920
median5525438
Q35528676
95-th percentile5534266
Maximum5536457
Range22240
Interquartile range (IQR)6756

Descriptive statistics

Standard deviation4710.352306
Coefficient of variation (CV)0.0008524221356
Kurtosis-0.8168962561
Mean5525844.66
Median Absolute Deviation (MAD)3328
Skewness0.3351048887
Sum4.456124021 × 1012
Variance22187418.85
MonotonicityNot monotonic
2022-12-27T17:54:43.425425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
552687712635
 
1.6%
553476610223
 
1.3%
55241019225
 
1.1%
55286766342
 
0.8%
55267066194
 
0.8%
55190266020
 
0.7%
55196475880
 
0.7%
55282065688
 
0.7%
55224725250
 
0.7%
55253275208
 
0.6%
Other values (1398)733750
91.0%
ValueCountFrequency (%)
55142171
 
< 0.1%
55143661
 
< 0.1%
55155258
 
< 0.1%
551579012
 
< 0.1%
55164866
 
< 0.1%
551669014
 
< 0.1%
551686915
 
< 0.1%
55170466
 
< 0.1%
55171976
 
< 0.1%
5517980160
< 0.1%
ValueCountFrequency (%)
5536457848
0.1%
5536440576
0.1%
55363721120
0.1%
5536340108
 
< 0.1%
553633836
 
< 0.1%
5536321656
0.1%
5536284432
 
0.1%
553625432
 
< 0.1%
5536175188
 
< 0.1%
553616748
 
< 0.1%

centre.geographic.latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1376
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.86966864
Minimum49.76587
Maximum49.96631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:43.559448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum49.76587
5-th percentile49.81006
Q149.83364
median49.86686
Q349.89404
95-th percentile49.94602
Maximum49.96631
Range0.20044
Interquartile range (IQR)0.0604

Descriptive statistics

Standard deviation0.0425096182
Coefficient of variation (CV)0.0008524142903
Kurtosis-0.8168163374
Mean49.86966864
Median Absolute Deviation (MAD)0.03039
Skewness0.3314309857
Sum40215648.84
Variance0.00180706764
MonotonicityNot monotonic
2022-12-27T17:54:43.681427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.8775912635
 
1.6%
49.9488210208
 
1.3%
49.852769696
 
1.2%
49.893496342
 
0.8%
49.876186194
 
0.8%
49.80866020
 
0.7%
49.813445880
 
0.7%
49.891525688
 
0.7%
49.838395250
 
0.7%
49.865335208
 
0.6%
Other values (1366)733294
90.9%
ValueCountFrequency (%)
49.765871
 
< 0.1%
49.766961
 
< 0.1%
49.777918
 
< 0.1%
49.7803312
 
< 0.1%
49.786016
 
< 0.1%
49.7880614
 
< 0.1%
49.789615
 
< 0.1%
49.791136
 
< 0.1%
49.792436
 
< 0.1%
49.79883160
< 0.1%
ValueCountFrequency (%)
49.96631848
0.1%
49.96617576
0.1%
49.965551120
0.1%
49.9652536
 
< 0.1%
49.96519108
 
< 0.1%
49.96501656
0.1%
49.96431432
 
0.1%
49.9640432
 
< 0.1%
49.96379188
 
< 0.1%
49.96345464
0.1%

centre.geographic.longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1447
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.1161436
Minimum-97.32078
Maximum-96.96269
Zeros0
Zeros (%)0.0%
Negative806415
Negative (%)100.0%
Memory size12.3 MiB
2022-12-27T17:54:43.899335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-97.32078
5-th percentile-97.27176
Q1-97.16777
median-97.09314
Q3-97.05475
95-th percentile-97.02525
Maximum-96.96269
Range0.35809
Interquartile range (IQR)0.11302

Descriptive statistics

Standard deviation0.07608137592
Coefficient of variation (CV)-0.0007834060651
Kurtosis-0.2989308886
Mean-97.1161436
Median Absolute Deviation (MAD)0.04659
Skewness-0.7378167741
Sum-78315914.94
Variance0.005788375761
MonotonicityNot monotonic
2022-12-27T17:54:44.028420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.0325312635
 
1.6%
-97.0480810208
 
1.3%
-97.040769225
 
1.1%
-97.015676342
 
0.8%
-97.039866194
 
0.8%
-97.130726020
 
0.7%
-97.085455880
 
0.7%
-97.152235688
 
0.7%
-97.052975473
 
0.7%
-97.057555250
 
0.7%
Other values (1437)733500
91.0%
ValueCountFrequency (%)
-97.320782223
0.3%
-97.317687
 
< 0.1%
-97.3156168
 
< 0.1%
-97.31544184
 
< 0.1%
-97.3154228
 
< 0.1%
-97.31497141
 
< 0.1%
-97.31486154
 
< 0.1%
-97.31423320
 
< 0.1%
-97.31403306
 
< 0.1%
-97.313332198
0.3%
ValueCountFrequency (%)
-96.9626915
 
< 0.1%
-96.96296455
 
0.1%
-96.96326125
 
< 0.1%
-96.96358410
 
0.1%
-96.96379310
 
< 0.1%
-96.9641220
 
< 0.1%
-96.9643595
 
< 0.1%
-96.967081400
0.2%
-96.96751100
 
< 0.1%
-96.9732512
 
< 0.1%

Bus_Variant
Categorical

HIGH CARDINALITY

Distinct242
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
93-0-H
 
48200
49-1-D
 
43596
16-0-B
 
32659
95-0-pp
 
30527
16-0-M
 
26730
Other values (237)
624703 

Length

Max length7
Median length6
Mean length6.06718253
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45-0-K
2nd row45-0-K
3rd row45-0-K
4th row45-0-K
5th row45-0-K

Common Values

ValueCountFrequency (%)
93-0-H48200
 
6.0%
49-1-D43596
 
5.4%
16-0-B32659
 
4.0%
95-0-pp30527
 
3.8%
16-0-M26730
 
3.3%
95-1-R24258
 
3.0%
95-0-S21805
 
2.7%
16-1-L17780
 
2.2%
42-1-D17364
 
2.2%
58-1-D16764
 
2.1%
Other values (232)526732
65.3%

Length

2022-12-27T17:54:44.162427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
93-0-h48200
 
6.0%
49-1-d43596
 
5.4%
16-0-b32659
 
4.0%
95-0-pp30527
 
3.8%
16-0-m26730
 
3.3%
95-1-r24258
 
3.0%
95-0-s21805
 
2.7%
16-1-l17780
 
2.2%
42-1-d17364
 
2.2%
58-1-d16764
 
2.1%
Other values (232)526732
65.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Variants_Serviced_Count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.04669432
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:44.246427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.196332845
Coefficient of variation (CV)0.7208904518
Kurtosis71.2140043
Mean3.04669432
Median Absolute Deviation (MAD)1
Skewness5.944794601
Sum2456900
Variance4.823877965
MonotonicityNot monotonic
2022-12-27T17:54:44.339423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2236571
29.3%
4162703
20.2%
1153867
19.1%
3125034
15.5%
559071
 
7.3%
643831
 
5.4%
716070
 
2.0%
96118
 
0.8%
28474
 
0.1%
8436
 
0.1%
Other values (12)2240
 
0.3%
ValueCountFrequency (%)
1153867
19.1%
2236571
29.3%
3125034
15.5%
4162703
20.2%
559071
 
7.3%
643831
 
5.4%
716070
 
2.0%
8436
 
0.1%
96118
 
0.8%
10121
 
< 0.1%
ValueCountFrequency (%)
36410
0.1%
35218
< 0.1%
31172
 
< 0.1%
30134
 
< 0.1%
29324
< 0.1%
28474
0.1%
27311
< 0.1%
24124
 
< 0.1%
23105
 
< 0.1%
20127
 
< 0.1%

Number_of_Stoppages
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.5427528
Minimum9
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:44.448423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile29
Q152
median68
Q396
95-th percentile153
Maximum186
Range177
Interquartile range (IQR)44

Descriptive statistics

Standard deviation35.72414325
Coefficient of variation (CV)0.4667214327
Kurtosis-0.3594675121
Mean76.5427528
Median Absolute Deviation (MAD)17
Skewness0.6894685952
Sum61725224
Variance1276.214411
MonotonicityNot monotonic
2022-12-27T17:54:44.557422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15359389
 
7.4%
6152088
 
6.5%
8249569
 
6.1%
11048200
 
6.0%
12443716
 
5.4%
5336230
 
4.5%
5234366
 
4.3%
8330562
 
3.8%
6629507
 
3.7%
6221520
 
2.7%
Other values (87)401268
49.8%
ValueCountFrequency (%)
9983
 
0.1%
162201
 
0.3%
171038
 
0.1%
183
 
< 0.1%
19748
 
0.1%
204319
0.5%
212068
 
0.3%
226123
0.8%
241586
 
0.2%
255914
0.7%
ValueCountFrequency (%)
18680
 
< 0.1%
180140
 
< 0.1%
172100
 
< 0.1%
15359389
7.4%
147153
 
< 0.1%
14632
 
< 0.1%
145136
 
< 0.1%
14317780
 
2.2%
142128
 
< 0.1%
141119
 
< 0.1%

Types_of_Features_Count
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1800
Missing (%)0.2%
Memory size12.3 MiB
0.0
591794 
1.0
168647 
2.0
 
41035
3.0
 
3009
4.0
 
130

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
0.0591794
73.4%
1.0168647
 
20.9%
2.041035
 
5.1%
3.03009
 
0.4%
4.0130
 
< 0.1%
(Missing)1800
 
0.2%

Length

2022-12-27T17:54:45.055337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-27T17:54:45.126437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0591794
73.5%
1.0168647
 
21.0%
2.041035
 
5.1%
3.03009
 
0.4%
4.0130
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Destination_Name
Categorical

HIGH CARDINALITY

Distinct150
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Downtown
84532 
City Hall
 
39974
St. Vital Centre
 
32552
South St. Vital
 
30342
Balmoral Station
 
28345
Other values (145)
590670 

Length

Max length28
Median length11
Mean length12.52630965
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCity Hall
2nd rowPoint Douglas
3rd rowKildonan Place
4th rowMunroe
5th rowCrossroads Station

Common Values

ValueCountFrequency (%)
Downtown84532
 
10.5%
City Hall39974
 
5.0%
St. Vital Centre32552
 
4.0%
South St. Vital30342
 
3.8%
Balmoral Station28345
 
3.5%
Kildonan Place28090
 
3.5%
St. Boniface Industrial Park27820
 
3.4%
Polo Park25206
 
3.1%
Grace Hospital21767
 
2.7%
Southglen21451
 
2.7%
Other values (140)466336
57.8%

Length

2022-12-27T17:54:45.241429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park135902
 
8.7%
st111162
 
7.1%
downtown84532
 
5.4%
centre63516
 
4.1%
vital62894
 
4.0%
station47930
 
3.1%
city40301
 
2.6%
hall39974
 
2.6%
kildonan39138
 
2.5%
industrial37050
 
2.4%
Other values (173)892537
57.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Route_Name
Categorical

HIGH CARDINALITY

Distinct225
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
to Highbury & Southfields
 
48200
Dugald to Balmoral Station
 
43596
Selkirk-Osborne to Tyndall Park via Burrows
 
32659
to Polo Park
 
30527
Selkirk-Osborne to Tyndall Park via Manitoba
 
26730
Other values (220)
624703 

Length

Max length60
Median length26
Mean length29.21398039
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTalbot to Kildonan Place
2nd rowTalbot to Kildonan Place
3rd rowTalbot to Kildonan Place
4th rowTalbot to Kildonan Place
5th rowTalbot to Kildonan Place

Common Values

ValueCountFrequency (%)
to Highbury & Southfields48200
 
6.0%
Dugald to Balmoral Station43596
 
5.4%
Selkirk-Osborne to Tyndall Park via Burrows32659
 
4.0%
to Polo Park30527
 
3.8%
Selkirk-Osborne to Tyndall Park via Manitoba26730
 
3.3%
to Unicity26168
 
3.2%
to Riverview24258
 
3.0%
to Shaftesbury Park21805
 
2.7%
to St. Vital Centre21367
 
2.6%
to Kildonan Place20507
 
2.5%
Other values (215)510598
63.3%

Length

2022-12-27T17:54:45.370393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to805359
21.3%
express187204
 
4.9%
via186522
 
4.9%
park180553
 
4.8%
124439
 
3.3%
st103140
 
2.7%
selkirk-osborne94187
 
2.5%
station89437
 
2.4%
dugald79890
 
2.1%
balmoral67907
 
1.8%
Other values (202)1866166
49.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Route_Number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.71088583
Minimum10
Maximum694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:45.482435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16
Q135
median57
Q392
95-th percentile96
Maximum694
Range684
Interquartile range (IQR)57

Descriptive statistics

Standard deviation98.5892654
Coefficient of variation (CV)1.337512964
Kurtosis31.67685615
Mean73.71088583
Median Absolute Deviation (MAD)30
Skewness5.522138611
Sum59441564
Variance9719.843252
MonotonicityNot monotonic
2022-12-27T17:54:45.590428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1694187
 
11.7%
9576590
 
9.5%
4969083
 
8.6%
9356267
 
7.0%
8344108
 
5.5%
4233954
 
4.2%
1531806
 
3.9%
5031784
 
3.9%
9629485
 
3.7%
5928496
 
3.5%
Other values (71)310655
38.5%
ValueCountFrequency (%)
1076
 
< 0.1%
11743
 
0.1%
127
 
< 0.1%
14150
 
< 0.1%
1531806
 
3.9%
1694187
11.7%
1787
 
< 0.1%
18281
 
< 0.1%
19153
 
< 0.1%
20258
 
< 0.1%
ValueCountFrequency (%)
6945572
0.7%
693645
 
0.1%
691769
 
0.1%
6906692
0.8%
6773454
0.4%
67612
 
< 0.1%
672587
 
0.1%
671429
 
0.1%
662437
 
0.1%
65010
 
< 0.1%

Variants_Count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.130321237
Minimum2
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:45.698428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q34
95-th percentile11
Maximum11
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.858363869
Coefficient of variation (CV)0.692043961
Kurtosis1.255578661
Mean4.130321237
Median Absolute Deviation (MAD)1
Skewness1.631578047
Sum3330753
Variance8.170244007
MonotonicityNot monotonic
2022-12-27T17:54:45.784438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3349102
43.3%
2219455
27.2%
1198677
 
12.2%
458063
 
7.2%
645335
 
5.6%
731959
 
4.0%
82760
 
0.3%
10743
 
0.1%
5321
 
< 0.1%
ValueCountFrequency (%)
2219455
27.2%
3349102
43.3%
458063
 
7.2%
5321
 
< 0.1%
645335
 
5.6%
731959
 
4.0%
82760
 
0.3%
10743
 
0.1%
1198677
 
12.2%
ValueCountFrequency (%)
1198677
 
12.2%
10743
 
0.1%
82760
 
0.3%
731959
 
4.0%
645335
 
5.6%
5321
 
< 0.1%
458063
 
7.2%
3349102
43.3%
2219455
27.2%

Route_Description
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Route 16 Selkirk-Osborne
94187 
Route 95 Tuxedo - Riverview
76590 
Route 49 Dugald
69083 
Route 93 South St. Vital - St. Marys
56267 
Route 83 Unicity - Strauss Dr - Murray Industrial Park
 
44108
Other values (76)
466180 

Length

Max length54
Median length26
Mean length28.24824935
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoute 45 Talbot
2nd rowRoute 45 Talbot
3rd rowRoute 45 Talbot
4th rowRoute 45 Talbot
5th rowRoute 45 Talbot

Common Values

ValueCountFrequency (%)
Route 16 Selkirk-Osborne94187
 
11.7%
Route 95 Tuxedo - Riverview76590
 
9.5%
Route 49 Dugald69083
 
8.6%
Route 93 South St. Vital - St. Marys56267
 
7.0%
Route 83 Unicity - Strauss Dr - Murray Industrial Park44108
 
5.5%
Route 42 Plessis Express33954
 
4.2%
Route 15 Sargent-Mountain31806
 
3.9%
Route 50 Archibald31784
 
3.9%
Route 96 St. Vital Centre - Windsor Park29485
 
3.7%
Route 59 South St. Anne's Express28496
 
3.5%
Other values (71)310655
38.5%

Length

2022-12-27T17:54:45.917426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
route806415
20.2%
358150
 
9.0%
express203344
 
5.1%
st194757
 
4.9%
1694187
 
2.4%
selkirk-osborne94187
 
2.4%
park87897
 
2.2%
vital85752
 
2.1%
south84763
 
2.1%
industrial77612
 
1.9%
Other values (172)1912957
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

coverage
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
regular
584456 
express
188079 
feeder
 
18615
super express
 
15265

Length

Max length13
Median length7
Mean length7.090493108
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular584456
72.5%
express188079
 
23.3%
feeder18615
 
2.3%
super express15265
 
1.9%

Length

2022-12-27T17:54:46.049433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-27T17:54:46.156423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
regular584456
71.1%
express203344
 
24.7%
feeder18615
 
2.3%
super15265
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

key
Categorical

HIGH CARDINALITY

Distinct16008
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
22722441-50
 
297
22722539-35
 
282
22720730-20
 
277
22721860-46
 
255
22722789-44
 
250
Other values (16003)
805054 

Length

Max length12
Median length11
Mean length10.86067471
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)< 0.1%

Sample

1st row22721732-6
2nd row22721732-6
3rd row22721732-6
4th row22721732-6
5th row22721732-6

Common Values

ValueCountFrequency (%)
22722441-50297
 
< 0.1%
22722539-35282
 
< 0.1%
22720730-20277
 
< 0.1%
22721860-46255
 
< 0.1%
22722789-44250
 
< 0.1%
22722777-42244
 
< 0.1%
22722163-36243
 
< 0.1%
22722448-62243
 
< 0.1%
22721329-40240
 
< 0.1%
22722882-2240
 
< 0.1%
Other values (15998)803844
99.7%

Length

2022-12-27T17:54:46.247427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22722441-50297
 
< 0.1%
22722539-35282
 
< 0.1%
22720730-20277
 
< 0.1%
22721860-46255
 
< 0.1%
22722789-44250
 
< 0.1%
22722777-42244
 
< 0.1%
22722163-36243
 
< 0.1%
22722448-62243
 
< 0.1%
22721329-40240
 
< 0.1%
22722882-2240
 
< 0.1%
Other values (15998)803844
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cancelled
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
False
806415 
ValueCountFrequency (%)
False806415
100.0%
2022-12-27T17:54:46.318423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

times.departure.scheduled
Categorical

HIGH CARDINALITY

Distinct7389
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2022-12-26T12:39:00
 
1484
2022-12-26T12:25:00
 
1218
2022-12-26T12:13:00
 
1169
2022-12-26T13:09:00
 
1132
2022-12-26T12:22:00
 
1131
Other values (7384)
800281 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row2022-12-26T12:24:00
2nd row2022-12-26T12:24:00
3rd row2022-12-26T12:24:00
4th row2022-12-26T12:24:00
5th row2022-12-26T12:24:00

Common Values

ValueCountFrequency (%)
2022-12-26T12:39:001484
 
0.2%
2022-12-26T12:25:001218
 
0.2%
2022-12-26T12:13:001169
 
0.1%
2022-12-26T13:09:001132
 
0.1%
2022-12-26T12:22:001131
 
0.1%
2022-12-26T12:29:001089
 
0.1%
2022-12-26T12:48:00979
 
0.1%
2022-12-26T13:02:00966
 
0.1%
2022-12-26T12:15:00952
 
0.1%
2022-12-26T12:44:00946
 
0.1%
Other values (7379)795349
98.6%

Length

2022-12-27T17:54:46.395425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-26t12:39:001484
 
0.2%
2022-12-26t12:25:001218
 
0.2%
2022-12-26t12:13:001169
 
0.1%
2022-12-26t13:09:001132
 
0.1%
2022-12-26t12:22:001131
 
0.1%
2022-12-26t12:29:001089
 
0.1%
2022-12-26t12:48:00979
 
0.1%
2022-12-26t13:02:00966
 
0.1%
2022-12-26t12:15:00952
 
0.1%
2022-12-26t12:44:00946
 
0.1%
Other values (7379)795349
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

times.departure.estimated
Categorical

HIGH CARDINALITY

Distinct7391
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2022-12-26T12:39:00
 
1484
2022-12-26T12:25:00
 
1218
2022-12-26T12:13:00
 
1142
2022-12-26T13:09:00
 
1132
2022-12-26T12:22:00
 
1131
Other values (7386)
800308 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st row2022-12-26T12:24:00
2nd row2022-12-26T12:24:00
3rd row2022-12-26T12:24:00
4th row2022-12-26T12:24:00
5th row2022-12-26T12:24:00

Common Values

ValueCountFrequency (%)
2022-12-26T12:39:001484
 
0.2%
2022-12-26T12:25:001218
 
0.2%
2022-12-26T12:13:001142
 
0.1%
2022-12-26T13:09:001132
 
0.1%
2022-12-26T12:22:001131
 
0.1%
2022-12-26T12:29:001089
 
0.1%
2022-12-26T12:15:00989
 
0.1%
2022-12-26T12:48:00979
 
0.1%
2022-12-26T13:02:00966
 
0.1%
2022-12-26T12:44:00946
 
0.1%
Other values (7381)795339
98.6%

Length

2022-12-27T17:54:46.498428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-26t12:39:001484
 
0.2%
2022-12-26t12:25:001218
 
0.2%
2022-12-26t12:13:001142
 
0.1%
2022-12-26t13:09:001132
 
0.1%
2022-12-26t12:22:001131
 
0.1%
2022-12-26t12:29:001089
 
0.1%
2022-12-26t12:15:00989
 
0.1%
2022-12-26t12:48:00979
 
0.1%
2022-12-26t13:02:00966
 
0.1%
2022-12-26t12:44:00946
 
0.1%
Other values (7381)795339
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

variant.key
Categorical

HIGH CARDINALITY

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
18-1-A
 
22419
14-1-E
 
17227
77-1-K
 
15549
15-0-W
 
15448
77-0-P
 
14902
Other values (103)
720870 

Length

Max length8
Median length6
Mean length6.253889127
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45-0-K
2nd row45-0-K
3rd row45-0-K
4th row45-0-K
5th row45-0-K

Common Values

ValueCountFrequency (%)
18-1-A22419
 
2.8%
14-1-E17227
 
2.1%
77-1-K15549
 
1.9%
15-0-W15448
 
1.9%
77-0-P14902
 
1.8%
17-1-MH14347
 
1.8%
47-1-U14092
 
1.7%
15-1-L13841
 
1.7%
11-0-C13712
 
1.7%
11-0-S13508
 
1.7%
Other values (98)651370
80.8%

Length

2022-12-27T17:54:46.621423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18-1-a22419
 
2.8%
14-1-e17227
 
2.1%
77-1-k15549
 
1.9%
15-0-w15448
 
1.9%
77-0-p14902
 
1.8%
17-1-mh14347
 
1.8%
47-1-u14092
 
1.7%
15-1-l13841
 
1.7%
11-0-c13712
 
1.7%
11-0-s13508
 
1.7%
Other values (98)651370
80.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

variant.name
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
North Main-Corydon to Assiniboine Park
 
22419
St. Mary's-Ellice to Ferry Road
 
17227
Crosstown North to Kildonan Place
 
15549
Sargent-Mountain to Airport via Wellington
 
15448
Crosstown North to Polo Park
 
14902
Other values (102)
720870 

Length

Max length67
Median length34
Mean length35.44341561
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTalbot to Kildonan Place
2nd rowTalbot to Kildonan Place
3rd rowTalbot to Kildonan Place
4th rowTalbot to Kildonan Place
5th rowTalbot to Kildonan Place

Common Values

ValueCountFrequency (%)
North Main-Corydon to Assiniboine Park22419
 
2.8%
St. Mary's-Ellice to Ferry Road17227
 
2.1%
Crosstown North to Kildonan Place15549
 
1.9%
Sargent-Mountain to Airport via Wellington15448
 
1.9%
Crosstown North to Polo Park14902
 
1.8%
McGregor to Misericordia Health Centre14347
 
1.8%
Transcona - Pembina to University of Manitoba via Downtown14092
 
1.7%
Sargent-Mountain to Mountain & Fife13841
 
1.7%
Portage-Kildonan to Crestview via13712
 
1.7%
Portage-Kildonan to St. Charles via13508
 
1.7%
Other values (97)651370
80.8%

Length

2022-12-27T17:54:46.764447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to806415
 
18.8%
via413540
 
9.6%
park129352
 
3.0%
st106996
 
2.5%
north105392
 
2.5%
portage-kildonan94737
 
2.2%
82457
 
1.9%
centre77192
 
1.8%
kildonan77153
 
1.8%
manitoba72391
 
1.7%
Other values (132)2329817
54.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bus.key
Real number (ℝ≥0)

HIGH CORRELATION

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.7491825
Minimum101
Maximum883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 MiB
2022-12-27T17:54:46.948427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile131
Q1320
median432
Q3801
95-th percentile871
Maximum883
Range782
Interquartile range (IQR)481

Descriptive statistics

Standard deviation266.6493154
Coefficient of variation (CV)0.5033501216
Kurtosis-1.551773477
Mean529.7491825
Median Absolute Deviation (MAD)271
Skewness-0.09395973129
Sum427197687
Variance71101.85743
MonotonicityNot monotonic
2022-12-27T17:54:47.083428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60510009
 
1.2%
8379851
 
1.2%
7709664
 
1.2%
8089621
 
1.2%
8399491
 
1.2%
3009456
 
1.2%
1919366
 
1.2%
1319142
 
1.1%
6359113
 
1.1%
7028759
 
1.1%
Other values (110)711943
88.3%
ValueCountFrequency (%)
1018488
1.1%
1067770
1.0%
1106386
0.8%
1255179
0.6%
1296656
0.8%
1319142
1.1%
1427179
0.9%
1454939
0.6%
1606808
0.8%
1618365
1.0%
ValueCountFrequency (%)
8836851
0.8%
8798453
1.0%
8786876
0.9%
8777947
1.0%
8755391
0.7%
8717298
0.9%
8707731
1.0%
8644264
0.5%
8638693
1.1%
8617112
0.9%

bus.bike-rack
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
False
668753 
True
137662 
ValueCountFrequency (%)
False668753
82.9%
True137662
 
17.1%
2022-12-27T17:54:47.174427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

bus.wifi
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
False
806415 
ValueCountFrequency (%)
False806415
100.0%
2022-12-27T17:54:47.216439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

times.arrival.scheduled
Categorical

HIGH CARDINALITY

Distinct7389
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2022-12-26T12:39:00
 
1484
2022-12-26T12:25:00
 
1218
2022-12-26T12:13:00
 
1169
2022-12-26T13:09:00
 
1132
2022-12-26T12:22:00
 
1131
Other values (7384)
800281 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row2022-12-26T12:24:00
2nd row2022-12-26T12:24:00
3rd row2022-12-26T12:24:00
4th row2022-12-26T12:24:00
5th row2022-12-26T12:24:00

Common Values

ValueCountFrequency (%)
2022-12-26T12:39:001484
 
0.2%
2022-12-26T12:25:001218
 
0.2%
2022-12-26T12:13:001169
 
0.1%
2022-12-26T13:09:001132
 
0.1%
2022-12-26T12:22:001131
 
0.1%
2022-12-26T12:29:001089
 
0.1%
2022-12-26T12:48:00979
 
0.1%
2022-12-26T13:02:00966
 
0.1%
2022-12-26T12:15:00952
 
0.1%
2022-12-26T12:44:00946
 
0.1%
Other values (7379)795349
98.6%

Length

2022-12-27T17:54:47.300428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-26t12:39:001484
 
0.2%
2022-12-26t12:25:001218
 
0.2%
2022-12-26t12:13:001169
 
0.1%
2022-12-26t13:09:001132
 
0.1%
2022-12-26t12:22:001131
 
0.1%
2022-12-26t12:29:001089
 
0.1%
2022-12-26t12:48:00979
 
0.1%
2022-12-26t13:02:00966
 
0.1%
2022-12-26t12:15:00952
 
0.1%
2022-12-26t12:44:00946
 
0.1%
Other values (7379)795349
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

times.arrival.estimated
Categorical

HIGH CARDINALITY

Distinct7391
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2022-12-26T12:39:00
 
1484
2022-12-26T12:25:00
 
1218
2022-12-26T12:13:00
 
1142
2022-12-26T13:09:00
 
1132
2022-12-26T12:22:00
 
1131
Other values (7386)
800308 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st row2022-12-26T12:24:00
2nd row2022-12-26T12:24:00
3rd row2022-12-26T12:24:00
4th row2022-12-26T12:24:00
5th row2022-12-26T12:24:00

Common Values

ValueCountFrequency (%)
2022-12-26T12:39:001484
 
0.2%
2022-12-26T12:25:001218
 
0.2%
2022-12-26T12:13:001142
 
0.1%
2022-12-26T13:09:001132
 
0.1%
2022-12-26T12:22:001131
 
0.1%
2022-12-26T12:29:001089
 
0.1%
2022-12-26T12:15:00989
 
0.1%
2022-12-26T12:48:00979
 
0.1%
2022-12-26T13:02:00966
 
0.1%
2022-12-26T12:44:00946
 
0.1%
Other values (7381)795339
98.6%

Length

2022-12-27T17:54:47.411441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-26t12:39:001484
 
0.2%
2022-12-26t12:25:001218
 
0.2%
2022-12-26t12:13:001142
 
0.1%
2022-12-26t13:09:001132
 
0.1%
2022-12-26t12:22:001131
 
0.1%
2022-12-26t12:29:001089
 
0.1%
2022-12-26t12:15:00989
 
0.1%
2022-12-26t12:48:00979
 
0.1%
2022-12-26t13:02:00966
 
0.1%
2022-12-26t12:44:00946
 
0.1%
Other values (7381)795339
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Delay_Time
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct476
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.228166639
Minimum-4018
Maximum916
Zeros754106
Zeros (%)93.5%
Negative10622
Negative (%)1.3%
Memory size12.3 MiB
2022-12-27T17:54:47.536422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4018
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum916
Range4934
Interquartile range (IQR)0

Descriptive statistics

Standard deviation57.13950206
Coefficient of variation (CV)7.905116874
Kurtosis633.4342755
Mean7.228166639
Median Absolute Deviation (MAD)0
Skewness-3.784144776
Sum5828902
Variance3264.922696
MonotonicityNot monotonic
2022-12-27T17:54:47.791165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0754106
93.5%
3490
 
0.1%
-2440
 
0.1%
65439
 
0.1%
24435
 
0.1%
63428
 
0.1%
83413
 
0.1%
114393
 
< 0.1%
55385
 
< 0.1%
-16381
 
< 0.1%
Other values (466)48505
 
6.0%
ValueCountFrequency (%)
-401818
 
< 0.1%
-106564
< 0.1%
-99994
< 0.1%
-926110
< 0.1%
-2378
 
< 0.1%
-21454
< 0.1%
-20954
< 0.1%
-18732
 
< 0.1%
-18252
< 0.1%
-17741
 
< 0.1%
ValueCountFrequency (%)
91660
< 0.1%
8319
 
< 0.1%
82815
 
< 0.1%
80328
 
< 0.1%
80112
 
< 0.1%
78948
 
< 0.1%
76721
 
< 0.1%
760135
< 0.1%
75432
 
< 0.1%
71449
 
< 0.1%

IsDelayed
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
False
754106 
True
 
52309
ValueCountFrequency (%)
False754106
93.5%
True52309
 
6.5%
2022-12-27T17:54:47.881178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Interactions

2022-12-27T17:54:19.160760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:11.271040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:17.714523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:22.772664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:28.259263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:33.655295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:39.320405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:46.135953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:52.264697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:57.947017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:03.222699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:08.621959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:13.716331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:19.534855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:12.395835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:18.114669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:23.202072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:28.658001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:34.040123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:39.726636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:46.673836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:52.709697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:58.329012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:03.712704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:09.013718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:14.119257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:20.024554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:12.821491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:18.485887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:23.579286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:29.069920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:34.485692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:40.188984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:47.196967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:53.136722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:58.787295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:04.096776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:09.380987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:14.529866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:20.402622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:13.216251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:18.891756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:24.009217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:29.522430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:34.904027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:40.698434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:47.632965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:53.586704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:59.182296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:04.479723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:09.788162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:14.936624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:20.851898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:13.673905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:19.244092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:24.702423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:29.973525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:35.296401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:41.178505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:48.051858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:53.986490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:59.594725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:04.844096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:10.203515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:15.353003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:21.282114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:14.098828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:19.629788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:25.051531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:30.389430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:35.698464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:41.618688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:48.521765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:54.359891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:59.962765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:05.237111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:10.548713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:15.727513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:21.729016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:14.533488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:20.060709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:25.472537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:30.813358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:36.134655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:42.031542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:49.023637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:54.801479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:00.420766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:05.609329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:10.945623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:16.229305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:22.205384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:15.028039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:20.464634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:25.845991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:31.191380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:36.622574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:42.554200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:49.520946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:55.203973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:00.869021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:06.050432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:11.305731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:16.754344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:22.578463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:15.602735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:20.856176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:26.223073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:31.598780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:37.063572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:43.052906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:50.015751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:55.713069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:01.235015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:06.770425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:11.758022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:17.157829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:22.990382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:16.042224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:21.212097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:26.599111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:31.991738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:37.549439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:43.526821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:50.437324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:56.120439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:01.664973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:07.129333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:12.115088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:17.574685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:23.389382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:16.482828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:21.630558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:27.034238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:32.389826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:37.947134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:44.188204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:50.945245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:56.574804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:02.046154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:07.520890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:12.472014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:17.975049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:23.841368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:16.895729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:22.017457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:27.459243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:32.777869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:38.355661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:44.758059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:51.388244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:57.011697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:02.439175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:07.906563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:12.844515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:18.360630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:24.321822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:17.315503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:22.399463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:27.865718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:33.273956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:38.856332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:45.225645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:51.807052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:53:57.483922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:02.851806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:08.247487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:13.235512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-27T17:54:18.744725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-12-27T17:54:48.099253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-27T17:54:48.359027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-27T17:54:48.628475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-27T17:54:49.054446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-27T17:54:49.311732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-27T17:54:27.244283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-27T17:54:30.387139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-27T17:54:35.759132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-27T17:54:37.604751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Stop_NameStop_Numberdirectionsidestreet.keystreet.namestreet.typecross-street.keycross-street.namecross-street.typecentre.utm.xcentre.utm.ycentre.geographic.latitudecentre.geographic.longitudeBus_VariantVariants_Serviced_CountNumber_of_StoppagesTypes_of_Features_CountDestination_NameRoute_NameRoute_NumberVariants_CountRoute_Descriptioncoveragekeycancelledtimes.departure.scheduledtimes.departure.estimatedvariant.keyvariant.namebus.keybus.bike-rackbus.wifitimes.arrival.scheduledtimes.arrival.estimatedDelay_TimeIsDelayed
0Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415245-0-K30.0573.0City HallTalbot to Kildonan Place45.03Route 45 Talbotregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
1Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415245-0-K30.0573.0Point DouglasTalbot to Kildonan Place45.03Route 45 Talbotregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
2Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415245-0-K30.0573.0Kildonan PlaceTalbot to Kildonan Place45.03Route 45 Talbotregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
3Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415245-0-K30.0573.0MunroeTalbot to Kildonan Place45.03Route 45 Talbotregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
4Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415245-0-K30.0573.0Crossroads StationTalbot to Kildonan Place45.03Route 45 Talbotregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
5Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415235-1-C30.0483.0City Hallto City Hall35.02Route 35 Maples Super Expresssuper express22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
6Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415235-1-C30.0483.0Maplesto City Hall35.02Route 35 Maples Super Expresssuper express22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
7Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415235-1-C30.0483.0Downtownto City Hall35.02Route 35 Maples Super Expresssuper express22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
8Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415235-1-C30.0483.0Health Sciences Centreto City Hall35.02Route 35 Maples Super Expresssuper express22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False
9Eastbound Graham at Smith10615.0EastboundNearside1533.0Graham AvenueAvenue3371.0Smith StreetStreet633485.05528315.049.89232-97.1415216-0-B30.01533.0FifeSelkirk-Osborne to Tyndall Park via Burrows16.011Route 16 Selkirk-Osborneregular22721732-6False2022-12-26T12:24:002022-12-26T12:24:0045-0-KTalbot to Kildonan Place359.0FalseFalse2022-12-26T12:24:002022-12-26T12:24:000.0False

Last rows

Stop_NameStop_Numberdirectionsidestreet.keystreet.namestreet.typecross-street.keycross-street.namecross-street.typecentre.utm.xcentre.utm.ycentre.geographic.latitudecentre.geographic.longitudeBus_VariantVariants_Serviced_CountNumber_of_StoppagesTypes_of_Features_CountDestination_NameRoute_NameRoute_NumberVariants_CountRoute_Descriptioncoveragekeycancelledtimes.departure.scheduledtimes.departure.estimatedvariant.keyvariant.namebus.keybus.bike-rackbus.wifitimes.arrival.scheduledtimes.arrival.estimatedDelay_TimeIsDelayed
806405Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22720722-16False2022-12-26T11:29:442022-12-26T11:29:44BLUE-1-DBLUE to Downtown397.0TrueFalse2022-12-26T11:29:442022-12-26T11:29:440.0False
806406Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22722501-6False2022-12-26T13:35:272022-12-26T13:35:2715-0-WSargent-Mountain to Airport via Wellington161.0TrueFalse2022-12-26T13:35:272022-12-26T13:35:270.0False
806407Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22721276-47False2022-12-26T13:23:012022-12-26T13:23:0179-1-KCharleswood to Polo Park via Kenaston432.0TrueFalse2022-12-26T13:23:012022-12-26T13:23:010.0False
806408Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22722883-27False2022-12-26T13:26:402022-12-26T13:26:4010-0-SPSt. Boniface-Wolseley to St. Boniface via Provencher171.0FalseFalse2022-12-26T13:26:402022-12-26T13:26:400.0False
806409Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22722770-114False2022-12-26T13:16:222022-12-26T13:16:2211-1-DPortage-Kildonan to North Kildonan via Donwood635.0FalseFalse2022-12-26T13:16:222022-12-26T13:16:220.0False
806410Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22720726-15False2022-12-26T12:13:352022-12-26T12:13:35BLUE-1-DBLUE to Downtown376.0TrueFalse2022-12-26T12:13:352022-12-26T12:13:350.0False
806411Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22722250-4False2022-12-26T13:28:262022-12-26T13:28:2618-0-JNorth Main-Corydon to Garden City Centre via Jefferson778.0FalseFalse2022-12-26T13:28:262022-12-26T13:28:260.0False
806412Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22722094-27False2022-12-26T13:05:222022-12-26T13:05:2224-1-PNess Express to Polo Park837.0FalseFalse2022-12-26T13:05:222022-12-26T13:05:220.0False
806413Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22721967-56False2022-12-26T12:20:002022-12-26T12:20:0033-1-D2to Downtown336.0FalseFalse2022-12-26T12:20:002022-12-26T12:20:000.0False
806414Southbound Buchanan at Peltier20439.0SouthboundNearside Opposite547.0Buchanan BoulevardBoulevard2817.0Peltier AvenueAvenue620978.05528227.049.89419-97.315683-0-U1.0720.0Crestviewto Unicity83.06Route 83 Unicity - Strauss Dr - Murray Industrial Parkregular22721897-56False2022-12-26T12:02:492022-12-26T12:02:4938-1-FSalter to The Forks419.0TrueFalse2022-12-26T12:02:492022-12-26T12:02:490.0False

Duplicate rows

Most frequently occurring

Stop_NameStop_Numberdirectionsidestreet.keystreet.namestreet.typecross-street.keycross-street.namecross-street.typecentre.utm.xcentre.utm.ycentre.geographic.latitudecentre.geographic.longitudeBus_VariantVariants_Serviced_CountNumber_of_StoppagesTypes_of_Features_CountDestination_NameRoute_NameRoute_NumberVariants_CountRoute_Descriptioncoveragekeycancelledtimes.departure.scheduledtimes.departure.estimatedvariant.keyvariant.namebus.keybus.bike-rackbus.wifitimes.arrival.scheduledtimes.arrival.estimatedDelay_TimeIsDelayed# duplicates
3553Eastbound Camiel Sys at Ray Marius50994.0EastboundNearside616.0Camiel Sys StreetStreet3000.0Ray Marius RoadRoad640833.05526706.049.87618-97.0398649-1-D4.01240.0Balmoral StationDugald to Balmoral Station49.03Route 49 Dugaldregular22722448-9False2022-12-26T11:45:412022-12-26T11:45:4116-1-VSelkirk-Osborne to St.Vital Centre735.0FalseFalse2022-12-26T11:45:412022-12-26T11:45:410.0False12
3871Eastbound Camiel Sys at Ray Marius50994.0EastboundNearside616.0Camiel Sys StreetStreet3000.0Ray Marius RoadRoad640833.05526706.049.87618-97.0398649-1-D4.01240.0DowntownDugald to Balmoral Station49.03Route 49 Dugaldregular22722448-9False2022-12-26T11:45:412022-12-26T11:45:4116-1-VSelkirk-Osborne to St.Vital Centre735.0FalseFalse2022-12-26T11:45:412022-12-26T11:45:410.0False12
4189Eastbound Camiel Sys at Ray Marius50994.0EastboundNearside616.0Camiel Sys StreetStreet3000.0Ray Marius RoadRoad640833.05526706.049.87618-97.0398649-1-D4.01240.0St. Boniface Industrial ParkDugald to Balmoral Station49.03Route 49 Dugaldregular22722448-9False2022-12-26T11:45:412022-12-26T11:45:4116-1-VSelkirk-Osborne to St.Vital Centre735.0FalseFalse2022-12-26T11:45:412022-12-26T11:45:410.0False12
5904Eastbound Portage at Donald (Canada Life Centre)10582.0EastboundNearside2903.0Portage AvenueAvenue1070.0Donald StreetStreet633334.05528442.049.89350-97.1435841-0-G35.0753.0City HallHenderson Express to Glenway41.03Route 41 Henderson Expressexpress22722539-35False2022-12-26T13:06:302022-12-26T13:06:3015-1-LSargent-Mountain to Mountain & Fife161.0TrueFalse2022-12-26T13:06:302022-12-26T13:06:300.0False9
5905Eastbound Portage at Donald (Canada Life Centre)10582.0EastboundNearside2903.0Portage AvenueAvenue1070.0Donald StreetStreet633334.05528442.049.89350-97.1435841-0-G35.0753.0DowntownHenderson Express to Glenway41.03Route 41 Henderson Expressexpress22722539-35False2022-12-26T13:06:302022-12-26T13:06:3015-1-LSargent-Mountain to Mountain & Fife161.0TrueFalse2022-12-26T13:06:302022-12-26T13:06:300.0False9
5906Eastbound Portage at Donald (Canada Life Centre)10582.0EastboundNearside2903.0Portage AvenueAvenue1070.0Donald StreetStreet633334.05528442.049.89350-97.1435841-0-G35.0753.0GlenwayHenderson Express to Glenway41.03Route 41 Henderson Expressexpress22722539-35False2022-12-26T13:06:302022-12-26T13:06:3015-1-LSargent-Mountain to Mountain & Fife161.0TrueFalse2022-12-26T13:06:302022-12-26T13:06:300.0False9
5907Eastbound Portage at Donald (Canada Life Centre)10582.0EastboundNearside2903.0Portage AvenueAvenue1070.0Donald StreetStreet633334.05528442.049.89350-97.1435841-0-G35.0753.0Whellams LaneHenderson Express to Glenway41.03Route 41 Henderson Expressexpress22722539-35False2022-12-26T13:06:302022-12-26T13:06:3015-1-LSargent-Mountain to Mountain & Fife161.0TrueFalse2022-12-26T13:06:302022-12-26T13:06:300.0False9
15Eastbound Aldgate at Abbotsfield50947.0EastboundNearside Opposite52.0Aldgate RoadRoad1.0Abbotsfield DriveDrive638266.05519550.049.81245-97.0781293-0-H4.01100.0Dakotato Highbury & Southfields93.02Route 93 South St. Vital - St. Marysregular22720729-35False2022-12-26T12:56:352022-12-26T12:56:35BLUE-1-DBLUE to Downtown379.0TrueFalse2022-12-26T12:56:352022-12-26T12:56:350.0False8
50Eastbound Aldgate at Abbotsfield50947.0EastboundNearside Opposite52.0Aldgate RoadRoad1.0Abbotsfield DriveDrive638266.05519550.049.81245-97.0781293-0-H4.01100.0Paddingtonto Highbury & Southfields93.02Route 93 South St. Vital - St. Marysregular22720729-35False2022-12-26T12:56:352022-12-26T12:56:35BLUE-1-DBLUE to Downtown379.0TrueFalse2022-12-26T12:56:352022-12-26T12:56:350.0False8
85Eastbound Aldgate at Abbotsfield50947.0EastboundNearside Opposite52.0Aldgate RoadRoad1.0Abbotsfield DriveDrive638266.05519550.049.81245-97.0781293-0-H4.01100.0South St. Vitalto Highbury & Southfields93.02Route 93 South St. Vital - St. Marysregular22720729-35False2022-12-26T12:56:352022-12-26T12:56:35BLUE-1-DBLUE to Downtown379.0TrueFalse2022-12-26T12:56:352022-12-26T12:56:350.0False8